Heretofore, intrusion detection systems and property monitoring systems have only been able to provide binary on/off alert information to users. Even sophisticated systems which employ multiple sensors can only resolve an alert to a particular sector of the area under surveillance. Existing systems are not able to determine, and convey to system users, the precise location, identity, and movement of one or more intruders. This invention provides this capability and more, by combining analog and broadband sensors with the intelligence of a class of computer called a neural network, to bring about a fundamentally new type of intelligent area monitoring system.
The neural network computer uses a different principle to process information than does a traditional rule-based computer. The neural network is a connection-based computer, meaning that it processes information based on the connections between many small processors; as opposed to traditional computer processors which apply a sequence of rules to input information. The neural network processor works somewhat like a biological neural system in that it compares the inputs on each neuron to many other neurons, and formulates its output based on the aggregate of the inputs.
Neural network processors appear destined to dramatically expand the range of uses for computers. Many of the artificial intelligence applications that the computer industry has been striving in vain to achieve using rule-based computers, seem to come naturally to connection-based computers. Neural network processors not only facilitate a new generation of intrusion detection and computer monitoring systems, they allow commercial implementations of such systems to provide new functionality. Examples of new functionality include authentication of residents or employees by pattern matching their speech and optical images; speech recognition such that the system would alert another resident if someone were to call for help or cry; and behavior modeling so that the system would be able to issue an alert if someone were to fall in the shower, or a child were to walk toward the street.
The present invention employs multiple sensors of various types arrayed around the area to be monitored. Sensors are commercially available to detect sound, vibration, and emissions of various segments of the electromagnetic spectrum such as infrared, visual, and microwave. Prior art references of security assessment systems, intrusion detection systems, and computer monitoring systems, employ sensors with discrete outputs, usually binary, and thereby convey only on/off alert information to system users. U.S. Pat. No. 4,857,912 by Everett, Jr. et al., Aug. 15, 1989, discusses sensors that have "an on and off state". The present invention can provide useful information based on input from such binary sensors; however, a fundamentally new type of intelligent area monitoring is achieved when using the analog output sensors described above. Analysis of the analog data by the neural network computer allows this invention to not only detect the presence of people, animals, or objects; but to deduce their precise location at any instance. Additionally, the neural network is able to infer the identity of people, animals, or objects, and convey this to users of the monitoring system.
Sensors can be classified in terms of their energy source and their output signals. The simplest of these are passive sensors which detect energy either emitted or reflected by objects within their field of view. Active sensors emit their own energy which is bounced back to the sensor from the objects within their field of view. The present invention is quite effective with passive sensors, active sensors, or a combination of the two types.
One of the shortcomings of existing intrusion detection systems is their propensity to give false alarms. Existing systems use rule-based algorithms to determine if an alert should be issued. Common commercial burglar alarms for home and business issue an alert if any sensor on the property exceeds a threshold value. More sophisticated systems, such as that described by Everett, Jr. et al., in U.S. Pat. No. 4,857,912, employ a two stage processing scheme whereby the "on" signals from multiple sensors are added together, and an overall alert is issued when the sum exceeds a threshold value. Even elaborate rule-based systems are typically unable to reduce false alarms to an acceptable level because the variability encountered even in nominal environments exceeds the logic of these systems.
The present invention does not suffer from these shortcomings because it is based on an entirely different principle than rule-based systems. Connection-based systems not only accommodate a much wider range of variability, but improve their accuracy and discrimination with regular use. More importantly however, this invention does not merely issue alerts, but displays the exact location of one or more intruders in a graphic image of the monitored area. This allows the user to understand exactly what has caused the alert, and thereby qualify whether the situation warrants a particular level of alert. The user can then correct the system if necessary so that subsequent similar situations will be correctly interpreted by the system.